Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations15000
Missing cells16924
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory313.2 B

Variable types

Numeric9
Categorical7

Alerts

deteriorated is highly overall correlated with heart_rateHigh correlation
heart_rate is highly overall correlated with deterioratedHigh correlation
age has 1210 (8.1%) missing values Missing
weight has 1264 (8.4%) missing values Missing
temperature has 1191 (7.9%) missing values Missing
heart_rate has 1206 (8.0%) missing values Missing
systolic_bp has 1245 (8.3%) missing values Missing
diastolic_bp has 1226 (8.2%) missing values Missing
oxygen_saturation has 1171 (7.8%) missing values Missing
respiration_rate has 1211 (8.1%) missing values Missing
blood_glucose has 1237 (8.2%) missing values Missing
pain_level has 1194 (8.0%) missing values Missing
vomiting has 1215 (8.1%) missing values Missing
diarrhea has 1227 (8.2%) missing values Missing
fatigue_level has 1175 (7.8%) missing values Missing
sleep_quality has 1152 (7.7%) missing values Missing

Reproduction

Analysis started2025-06-16 04:45:30.503663
Analysis finished2025-06-16 04:45:51.937003
Duration21.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Missing 

Distinct66
Distinct (%)0.5%
Missing1210
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean52.539159
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:52.082668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23
Q136
median53
Q369
95-th percentile82
Maximum85
Range65
Interquartile range (IQR)33

Descriptive statistics

Standard deviation18.940323
Coefficient of variation (CV)0.36049916
Kurtosis-1.194387
Mean52.539159
Median Absolute Deviation (MAD)16
Skewness-0.0071857593
Sum724515
Variance358.73583
MonotonicityNot monotonic
2025-06-16T04:45:52.274338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 248
 
1.7%
76 239
 
1.6%
52 239
 
1.6%
39 236
 
1.6%
78 236
 
1.6%
62 235
 
1.6%
58 233
 
1.6%
66 230
 
1.5%
23 228
 
1.5%
55 225
 
1.5%
Other values (56) 11441
76.3%
(Missing) 1210
 
8.1%
ValueCountFrequency (%)
20 184
1.2%
21 206
1.4%
22 202
1.3%
23 228
1.5%
24 201
1.3%
25 211
1.4%
26 193
1.3%
27 216
1.4%
28 218
1.5%
29 208
1.4%
ValueCountFrequency (%)
85 178
1.2%
84 214
1.4%
83 216
1.4%
82 190
1.3%
81 182
1.2%
80 208
1.4%
79 206
1.4%
78 236
1.6%
77 248
1.7%
76 239
1.6%

weight
Real number (ℝ)

Missing 

Distinct901
Distinct (%)6.6%
Missing1264
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean75.068062
Minimum30
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:52.476666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile34.6
Q152.1
median75.1
Q397.9
95-th percentile115.6
Maximum120
Range90
Interquartile range (IQR)45.8

Descriptive statistics

Standard deviation26.101017
Coefficient of variation (CV)0.34769803
Kurtosis-1.2159799
Mean75.068062
Median Absolute Deviation (MAD)22.9
Skewness-0.0048519327
Sum1031134.9
Variance681.2631
MonotonicityNot monotonic
2025-06-16T04:45:52.700211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97.2 29
 
0.2%
48.5 28
 
0.2%
110.5 28
 
0.2%
44.7 26
 
0.2%
35 26
 
0.2%
56.6 25
 
0.2%
50.3 25
 
0.2%
73.6 25
 
0.2%
117 25
 
0.2%
40.3 24
 
0.2%
Other values (891) 13475
89.8%
(Missing) 1264
 
8.4%
ValueCountFrequency (%)
30 6
 
< 0.1%
30.1 23
0.2%
30.2 22
0.1%
30.3 17
0.1%
30.4 12
0.1%
30.5 7
 
< 0.1%
30.6 14
0.1%
30.7 20
0.1%
30.8 13
0.1%
30.9 18
0.1%
ValueCountFrequency (%)
120 6
 
< 0.1%
119.9 9
0.1%
119.8 16
0.1%
119.7 10
0.1%
119.6 14
0.1%
119.5 20
0.1%
119.4 15
0.1%
119.3 20
0.1%
119.2 14
0.1%
119.1 10
0.1%

temperature
Real number (ℝ)

Missing 

Distinct56
Distinct (%)0.4%
Missing1191
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean36.886386
Minimum33.5
Maximum39.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:52.893985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33.5
5-th percentile35.7
Q136.4
median36.9
Q337.4
95-th percentile38.1
Maximum39.8
Range6.3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7201455
Coefficient of variation (CV)0.019523341
Kurtosis-0.030292317
Mean36.886386
Median Absolute Deviation (MAD)0.5
Skewness-0.016605015
Sum509364.1
Variance0.51860954
MonotonicityNot monotonic
2025-06-16T04:45:53.088974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.9 774
 
5.2%
37.2 752
 
5.0%
36.8 742
 
4.9%
37.1 731
 
4.9%
37 730
 
4.9%
36.7 728
 
4.9%
36.6 703
 
4.7%
36.5 677
 
4.5%
37.3 631
 
4.2%
36.4 619
 
4.1%
Other values (46) 6722
44.8%
(Missing) 1191
 
7.9%
ValueCountFrequency (%)
33.5 1
 
< 0.1%
34.2 1
 
< 0.1%
34.3 1
 
< 0.1%
34.4 3
 
< 0.1%
34.5 3
 
< 0.1%
34.6 4
 
< 0.1%
34.7 6
 
< 0.1%
34.8 12
0.1%
34.9 16
0.1%
35 21
0.1%
ValueCountFrequency (%)
39.8 1
 
< 0.1%
39.5 1
 
< 0.1%
39.4 2
 
< 0.1%
39.3 1
 
< 0.1%
39.2 1
 
< 0.1%
39.1 7
 
< 0.1%
39 10
 
0.1%
38.9 14
 
0.1%
38.8 17
 
0.1%
38.7 43
0.3%

heart_rate
Real number (ℝ)

High correlation  Missing 

Distinct81
Distinct (%)0.6%
Missing1206
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean94.34428
Minimum60
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:53.281119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile63
Q179
median95
Q3110
95-th percentile126
Maximum140
Range80
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.396712
Coefficient of variation (CV)0.205595
Kurtosis-0.81183377
Mean94.34428
Median Absolute Deviation (MAD)15
Skewness0.079566794
Sum1301385
Variance376.23244
MonotonicityNot monotonic
2025-06-16T04:45:53.828602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 259
 
1.7%
99 258
 
1.7%
119 258
 
1.7%
91 257
 
1.7%
97 252
 
1.7%
101 252
 
1.7%
85 250
 
1.7%
90 249
 
1.7%
109 244
 
1.6%
112 244
 
1.6%
Other values (71) 11271
75.1%
(Missing) 1206
 
8.0%
ValueCountFrequency (%)
60 171
1.1%
61 177
1.2%
62 171
1.1%
63 199
1.3%
64 184
1.2%
65 181
1.2%
66 183
1.2%
67 192
1.3%
68 197
1.3%
69 184
1.2%
ValueCountFrequency (%)
140 46
0.3%
139 36
0.2%
138 51
0.3%
137 67
0.4%
136 43
0.3%
135 43
0.3%
134 38
0.3%
133 45
0.3%
132 47
0.3%
131 48
0.3%

systolic_bp
Real number (ℝ)

Missing 

Distinct41
Distinct (%)0.3%
Missing1245
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean110.01839
Minimum90
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:54.040342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile92
Q1100
median110
Q3120
95-th percentile129
Maximum130
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.814567
Coefficient of variation (CV)0.1073872
Kurtosis-1.1950296
Mean110.01839
Median Absolute Deviation (MAD)10
Skewness0.006868645
Sum1513303
Variance139.584
MonotonicityNot monotonic
2025-06-16T04:45:54.252409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
124 369
 
2.5%
99 364
 
2.4%
102 363
 
2.4%
115 361
 
2.4%
130 359
 
2.4%
111 356
 
2.4%
114 355
 
2.4%
105 354
 
2.4%
107 350
 
2.3%
97 350
 
2.3%
Other values (31) 10174
67.8%
(Missing) 1245
 
8.3%
ValueCountFrequency (%)
90 337
2.2%
91 314
2.1%
92 332
2.2%
93 332
2.2%
94 325
2.2%
95 346
2.3%
96 313
2.1%
97 350
2.3%
98 337
2.2%
99 364
2.4%
ValueCountFrequency (%)
130 359
2.4%
129 333
2.2%
128 336
2.2%
127 318
2.1%
126 339
2.3%
125 329
2.2%
124 369
2.5%
123 322
2.1%
122 321
2.1%
121 341
2.3%

diastolic_bp
Real number (ℝ)

Missing 

Distinct21
Distinct (%)0.2%
Missing1226
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean70.054886
Minimum60
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:54.487009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile61
Q165
median70
Q375
95-th percentile79
Maximum80
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0765376
Coefficient of variation (CV)0.086739668
Kurtosis-1.2188645
Mean70.054886
Median Absolute Deviation (MAD)5
Skewness-0.016285877
Sum964936
Variance36.924309
MonotonicityNot monotonic
2025-06-16T04:45:54.703622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
78 715
 
4.8%
61 712
 
4.7%
72 696
 
4.6%
63 680
 
4.5%
80 680
 
4.5%
75 679
 
4.5%
73 666
 
4.4%
71 657
 
4.4%
66 655
 
4.4%
77 653
 
4.4%
Other values (11) 6981
46.5%
(Missing) 1226
 
8.2%
ValueCountFrequency (%)
60 613
4.1%
61 712
4.7%
62 651
4.3%
63 680
4.5%
64 646
4.3%
65 635
4.2%
66 655
4.4%
67 620
4.1%
68 600
4.0%
69 644
4.3%
ValueCountFrequency (%)
80 680
4.5%
79 622
4.1%
78 715
4.8%
77 653
4.4%
76 653
4.4%
75 679
4.5%
74 647
4.3%
73 666
4.4%
72 696
4.6%
71 657
4.4%

oxygen_saturation
Real number (ℝ)

Missing 

Distinct151
Distinct (%)1.1%
Missing1171
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean92.533835
Minimum85
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:54.887359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile85.7
Q188.8
median92.6
Q396.3
95-th percentile99.3
Maximum100
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.3198551
Coefficient of variation (CV)0.04668406
Kurtosis-1.1792946
Mean92.533835
Median Absolute Deviation (MAD)3.7
Skewness-0.018544704
Sum1279650.4
Variance18.661148
MonotonicityNot monotonic
2025-06-16T04:45:55.104151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.3 121
 
0.8%
96.3 118
 
0.8%
85.8 115
 
0.8%
95.2 111
 
0.7%
93.7 109
 
0.7%
94.5 109
 
0.7%
97.8 109
 
0.7%
85.3 108
 
0.7%
89.6 108
 
0.7%
94.8 107
 
0.7%
Other values (141) 12714
84.8%
(Missing) 1171
 
7.8%
ValueCountFrequency (%)
85 58
0.4%
85.1 79
0.5%
85.2 101
0.7%
85.3 108
0.7%
85.4 81
0.5%
85.5 101
0.7%
85.6 96
0.6%
85.7 96
0.6%
85.8 115
0.8%
85.9 92
0.6%
ValueCountFrequency (%)
100 67
0.4%
99.9 94
0.6%
99.8 98
0.7%
99.7 101
0.7%
99.6 80
0.5%
99.5 103
0.7%
99.4 88
0.6%
99.3 84
0.6%
99.2 84
0.6%
99.1 101
0.7%

respiration_rate
Real number (ℝ)

Missing 

Distinct11
Distinct (%)0.1%
Missing1211
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean18.978824
Minimum14
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:55.253751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14
Q116
median19
Q322
95-th percentile24
Maximum24
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1735624
Coefficient of variation (CV)0.16721597
Kurtosis-1.2295892
Mean18.978824
Median Absolute Deviation (MAD)3
Skewness0.016458331
Sum261699
Variance10.071498
MonotonicityNot monotonic
2025-06-16T04:45:55.375639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
17 1315
8.8%
23 1278
8.5%
14 1269
8.5%
24 1262
8.4%
16 1261
8.4%
15 1260
8.4%
18 1260
8.4%
21 1234
8.2%
20 1222
8.1%
22 1218
8.1%
(Missing) 1211
8.1%
ValueCountFrequency (%)
14 1269
8.5%
15 1260
8.4%
16 1261
8.4%
17 1315
8.8%
18 1260
8.4%
19 1210
8.1%
20 1222
8.1%
21 1234
8.2%
22 1218
8.1%
23 1278
8.5%
ValueCountFrequency (%)
24 1262
8.4%
23 1278
8.5%
22 1218
8.1%
21 1234
8.2%
20 1222
8.1%
19 1210
8.1%
18 1260
8.4%
17 1315
8.8%
16 1261
8.4%
15 1260
8.4%

blood_glucose
Real number (ℝ)

Missing 

Distinct131
Distinct (%)1.0%
Missing1237
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean135.43421
Minimum70
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-06-16T04:45:55.532365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile76
Q1103
median136
Q3168
95-th percentile194
Maximum200
Range130
Interquartile range (IQR)65

Descriptive statistics

Standard deviation37.945728
Coefficient of variation (CV)0.28017832
Kurtosis-1.2073988
Mean135.43421
Median Absolute Deviation (MAD)33
Skewness-0.0077895033
Sum1863981
Variance1439.8783
MonotonicityNot monotonic
2025-06-16T04:45:55.755802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106 131
 
0.9%
180 127
 
0.8%
138 125
 
0.8%
167 125
 
0.8%
94 123
 
0.8%
104 123
 
0.8%
148 122
 
0.8%
87 122
 
0.8%
198 122
 
0.8%
122 119
 
0.8%
Other values (121) 12524
83.5%
(Missing) 1237
 
8.2%
ValueCountFrequency (%)
70 110
0.7%
71 113
0.8%
72 103
0.7%
73 93
0.6%
74 108
0.7%
75 109
0.7%
76 101
0.7%
77 99
0.7%
78 100
0.7%
79 82
0.5%
ValueCountFrequency (%)
200 110
0.7%
199 115
0.8%
198 122
0.8%
197 104
0.7%
196 97
0.6%
195 103
0.7%
194 111
0.7%
193 95
0.6%
192 119
0.8%
191 115
0.8%

pain_level
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing1194
Missing (%)8.0%
Memory size883.7 KiB
1.0
2816 
2.0
2807 
5.0
2748 
3.0
2738 
4.0
2697 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41418
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 2816
18.8%
2.0 2807
18.7%
5.0 2748
18.3%
3.0 2738
18.3%
4.0 2697
18.0%
(Missing) 1194
8.0%

Length

2025-06-16T04:45:55.925499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T04:45:56.040622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2816
20.4%
2.0 2807
20.3%
5.0 2748
19.9%
3.0 2738
19.8%
4.0 2697
19.5%

Most occurring characters

ValueCountFrequency (%)
. 13806
33.3%
0 13806
33.3%
1 2816
 
6.8%
2 2807
 
6.8%
5 2748
 
6.6%
3 2738
 
6.6%
4 2697
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 13806
33.3%
0 13806
33.3%
1 2816
 
6.8%
2 2807
 
6.8%
5 2748
 
6.6%
3 2738
 
6.6%
4 2697
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 13806
33.3%
0 13806
33.3%
1 2816
 
6.8%
2 2807
 
6.8%
5 2748
 
6.6%
3 2738
 
6.6%
4 2697
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 13806
33.3%
0 13806
33.3%
1 2816
 
6.8%
2 2807
 
6.8%
5 2748
 
6.6%
3 2738
 
6.6%
4 2697
 
6.5%

vomiting
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing1215
Missing (%)8.1%
Memory size883.8 KiB
0.0
6907 
1.0
6878 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41355
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6907
46.0%
1.0 6878
45.9%
(Missing) 1215
 
8.1%

Length

2025-06-16T04:45:56.191999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T04:45:56.298949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6907
50.1%
1.0 6878
49.9%

Most occurring characters

ValueCountFrequency (%)
0 20692
50.0%
. 13785
33.3%
1 6878
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20692
50.0%
. 13785
33.3%
1 6878
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20692
50.0%
. 13785
33.3%
1 6878
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20692
50.0%
. 13785
33.3%
1 6878
 
16.6%

diarrhea
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing1227
Missing (%)8.2%
Memory size883.8 KiB
0.0
6989 
1.0
6784 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41319
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6989
46.6%
1.0 6784
45.2%
(Missing) 1227
 
8.2%

Length

2025-06-16T04:45:56.411806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T04:45:56.501508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6989
50.7%
1.0 6784
49.3%

Most occurring characters

ValueCountFrequency (%)
0 20762
50.2%
. 13773
33.3%
1 6784
 
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20762
50.2%
. 13773
33.3%
1 6784
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20762
50.2%
. 13773
33.3%
1 6784
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20762
50.2%
. 13773
33.3%
1 6784
 
16.4%

fatigue_level
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing1175
Missing (%)7.8%
Memory size910.6 KiB
Mild
3524 
Moderate
3485 
No
3443 
Severe
3373 

Length

Max length8
Median length6
Mean length4.9981917
Min length2

Characters and Unicode

Total characters69100
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowMild
4th rowMild
5th rowModerate

Common Values

ValueCountFrequency (%)
Mild 3524
23.5%
Moderate 3485
23.2%
No 3443
23.0%
Severe 3373
22.5%
(Missing) 1175
 
7.8%

Length

2025-06-16T04:45:56.679893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T04:45:56.799743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mild 3524
25.5%
moderate 3485
25.2%
no 3443
24.9%
severe 3373
24.4%

Most occurring characters

ValueCountFrequency (%)
e 17089
24.7%
M 7009
10.1%
d 7009
10.1%
o 6928
10.0%
r 6858
9.9%
i 3524
 
5.1%
l 3524
 
5.1%
a 3485
 
5.0%
t 3485
 
5.0%
N 3443
 
5.0%
Other values (2) 6746
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17089
24.7%
M 7009
10.1%
d 7009
10.1%
o 6928
10.0%
r 6858
9.9%
i 3524
 
5.1%
l 3524
 
5.1%
a 3485
 
5.0%
t 3485
 
5.0%
N 3443
 
5.0%
Other values (2) 6746
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17089
24.7%
M 7009
10.1%
d 7009
10.1%
o 6928
10.0%
r 6858
9.9%
i 3524
 
5.1%
l 3524
 
5.1%
a 3485
 
5.0%
t 3485
 
5.0%
N 3443
 
5.0%
Other values (2) 6746
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17089
24.7%
M 7009
10.1%
d 7009
10.1%
o 6928
10.0%
r 6858
9.9%
i 3524
 
5.1%
l 3524
 
5.1%
a 3485
 
5.0%
t 3485
 
5.0%
N 3443
 
5.0%
Other values (2) 6746
 
9.8%

sleep_quality
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing1152
Missing (%)7.7%
Memory size897.1 KiB
Fair
4730 
Poor
4654 
Good
4464 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters55392
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFair
2nd rowGood
3rd rowPoor
4th rowFair
5th rowPoor

Common Values

ValueCountFrequency (%)
Fair 4730
31.5%
Poor 4654
31.0%
Good 4464
29.8%
(Missing) 1152
 
7.7%

Length

2025-06-16T04:45:56.933408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T04:45:57.026914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fair 4730
34.2%
poor 4654
33.6%
good 4464
32.2%

Most occurring characters

ValueCountFrequency (%)
o 18236
32.9%
r 9384
16.9%
a 4730
 
8.5%
F 4730
 
8.5%
i 4730
 
8.5%
P 4654
 
8.4%
G 4464
 
8.1%
d 4464
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 18236
32.9%
r 9384
16.9%
a 4730
 
8.5%
F 4730
 
8.5%
i 4730
 
8.5%
P 4654
 
8.4%
G 4464
 
8.1%
d 4464
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 18236
32.9%
r 9384
16.9%
a 4730
 
8.5%
F 4730
 
8.5%
i 4730
 
8.5%
P 4654
 
8.4%
G 4464
 
8.1%
d 4464
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 18236
32.9%
r 9384
16.9%
a 4730
 
8.5%
F 4730
 
8.5%
i 4730
 
8.5%
P 4654
 
8.4%
G 4464
 
8.1%
d 4464
 
8.1%

clinical_notes
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
"Severe headache, chest pain noted."
1132 
No remarkable symptoms today.
1105 
"Stable, no issues reported."
1096 
Severe headache with chest pain.
1094 
Patient reports feeling dizzy and shortness of breath.
1089 
Other values (9)
9484 

Length

Max length54
Median length36
Mean length33.804733
Min length28

Characters and Unicode

Total characters507071
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPatient resting comfortably.
2nd rowOccasional mild dizziness after walking.
3rd row"No complaints, stable condition."
4th row"No complaints, stable condition."
5th rowRestless sleep but otherwise well.

Common Values

ValueCountFrequency (%)
"Severe headache, chest pain noted." 1132
 
7.5%
No remarkable symptoms today. 1105
 
7.4%
"Stable, no issues reported." 1096
 
7.3%
Severe headache with chest pain. 1094
 
7.3%
Patient reports feeling dizzy and shortness of breath. 1089
 
7.3%
Feeling fine and well today. 1084
 
7.2%
"No complaints, stable condition." 1081
 
7.2%
Slight headache in the morning. 1076
 
7.2%
Complains of nausea and dizziness. 1074
 
7.2%
Chest tightness and dizziness. 1072
 
7.1%
Other values (4) 4097
27.3%

Length

2025-06-16T04:45:57.184233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 4319
 
6.1%
headache 3302
 
4.7%
chest 3298
 
4.7%
no 3282
 
4.6%
stable 3223
 
4.5%
dizziness 3178
 
4.5%
severe 2226
 
3.1%
pain 2226
 
3.1%
today 2189
 
3.1%
feeling 2173
 
3.1%
Other values (35) 41467
58.5%

Most occurring characters

ValueCountFrequency (%)
e 57046
 
11.3%
55883
 
11.0%
s 35213
 
6.9%
t 35213
 
6.9%
a 34410
 
6.8%
i 34069
 
6.7%
n 32171
 
6.3%
o 24673
 
4.9%
l 20096
 
4.0%
h 18471
 
3.6%
Other values (25) 159826
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 507071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 57046
 
11.3%
55883
 
11.0%
s 35213
 
6.9%
t 35213
 
6.9%
a 34410
 
6.8%
i 34069
 
6.7%
n 32171
 
6.3%
o 24673
 
4.9%
l 20096
 
4.0%
h 18471
 
3.6%
Other values (25) 159826
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 507071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 57046
 
11.3%
55883
 
11.0%
s 35213
 
6.9%
t 35213
 
6.9%
a 34410
 
6.8%
i 34069
 
6.7%
n 32171
 
6.3%
o 24673
 
4.9%
l 20096
 
4.0%
h 18471
 
3.6%
Other values (25) 159826
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 507071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 57046
 
11.3%
55883
 
11.0%
s 35213
 
6.9%
t 35213
 
6.9%
a 34410
 
6.8%
i 34069
 
6.7%
n 32171
 
6.3%
o 24673
 
4.9%
l 20096
 
4.0%
h 18471
 
3.6%
Other values (25) 159826
31.5%

deteriorated
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size849.7 KiB
0
12000 
1
3000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12000
80.0%
1 3000
 
20.0%

Length

2025-06-16T04:45:57.359292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T04:45:57.499437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12000
80.0%
1 3000
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 12000
80.0%
1 3000
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12000
80.0%
1 3000
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12000
80.0%
1 3000
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12000
80.0%
1 3000
 
20.0%

Interactions

2025-06-16T04:45:48.893504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:32.683519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:35.295739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:36.958282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:38.456544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:40.178818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:41.777272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:43.281832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:44.990179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:49.514336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:32.857711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:35.551594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:37.143991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:38.600788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:40.330602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:41.942623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:43.437239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:45.144253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:49.793146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:33.002584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:35.810421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:37.322038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:38.744618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:40.494771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:42.122992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:43.583657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:45.305170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:49.950557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:33.178770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:35.960404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:37.501718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:38.927816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:40.652976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:42.287571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:43.742181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:45.450787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:50.123202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:33.383106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:36.116639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:37.666250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:39.093704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:40.848165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:42.452335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:43.902184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:45.612939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:50.289617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:33.763938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:36.265297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:37.862583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:39.479455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:41.083377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:42.616780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:44.063325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:46.183705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:50.462671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:34.114388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:36.433240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:38.015038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:39.670547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:41.270623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:42.775609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:44.252859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:46.924475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:50.627456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:34.838143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:36.578929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:38.177749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:39.828418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:41.442444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:42.938393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:44.688104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:47.685669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:50.772158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:35.049605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:36.735019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:38.313291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:40.023985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:41.600934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:43.120919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:44.836624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T04:45:48.262884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-16T04:45:57.603646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageblood_glucoseclinical_notesdeteriorateddiarrheadiastolic_bpfatigue_levelheart_rateoxygen_saturationpain_levelrespiration_ratesleep_qualitysystolic_bptemperaturevomitingweight
age1.000-0.0010.0110.0000.000-0.0110.000-0.0080.0170.000-0.0110.000-0.0070.0090.0320.006
blood_glucose-0.0011.0000.0160.0160.006-0.0130.000-0.002-0.0190.007-0.0050.0000.0010.0010.015-0.015
clinical_notes0.0110.0161.0000.0070.0000.0000.0110.0090.0000.0100.0000.0080.0070.0000.0000.015
deteriorated0.0000.0160.0071.0000.0000.0000.0070.5600.0250.0000.0030.0000.0000.2310.0000.000
diarrhea0.0000.0060.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
diastolic_bp-0.011-0.0130.0000.0000.0001.0000.0060.007-0.0020.000-0.0170.0110.003-0.0100.0000.011
fatigue_level0.0000.0000.0110.0070.0000.0061.0000.0030.0170.0030.0130.0000.0070.0140.0000.000
heart_rate-0.008-0.0020.0090.5600.0000.0070.0031.000-0.0160.0190.0190.000-0.0060.0960.0000.005
oxygen_saturation0.017-0.0190.0000.0250.000-0.0020.017-0.0161.0000.013-0.0050.007-0.0050.0080.000-0.006
pain_level0.0000.0070.0100.0000.0000.0000.0030.0190.0131.0000.0000.0030.0120.0140.0000.000
respiration_rate-0.011-0.0050.0000.0030.000-0.0170.0130.019-0.0050.0001.0000.0000.009-0.0030.0000.004
sleep_quality0.0000.0000.0080.0000.0000.0110.0000.0000.0070.0030.0001.0000.0070.0000.0000.021
systolic_bp-0.0070.0010.0070.0000.0000.0030.007-0.006-0.0050.0120.0090.0071.0000.0160.000-0.004
temperature0.0090.0010.0000.2310.000-0.0100.0140.0960.0080.014-0.0030.0000.0161.0000.019-0.010
vomiting0.0320.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0191.0000.000
weight0.006-0.0150.0150.0000.0000.0110.0000.005-0.0060.0000.0040.021-0.004-0.0100.0001.000

Missing values

2025-06-16T04:45:51.009284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-16T04:45:51.260626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-16T04:45:51.718007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ageweighttemperatureheart_ratesystolic_bpdiastolic_bpoxygen_saturationrespiration_rateblood_glucosepain_levelvomitingdiarrheafatigue_levelsleep_qualityclinical_notesdeteriorated
031.0NaN37.366.0108.067.090.416.0188.01.01.00.0ModerateFairPatient resting comfortably.0
150.042.337.770.0107.066.099.817.0178.04.01.00.0ModerateGoodOccasional mild dizziness after walking.0
282.087.936.685.092.064.093.823.0165.02.01.00.0MildPoor"No complaints, stable condition."0
375.063.0NaN102.095.0NaN98.717.0127.03.01.01.0MildNaN"No complaints, stable condition."0
452.0105.936.967.090.067.085.323.0112.04.00.00.0ModerateFairRestless sleep but otherwise well.0
583.0114.136.760.0115.061.0NaN17.0185.02.00.01.0MildPoorOccasional mild dizziness after walking.0
635.074.336.591.0107.064.094.423.0187.04.00.00.0ModerateGoodSlight headache in the morning.0
778.0104.337.377.0105.067.093.724.0166.01.01.00.0SevereGoodChest tightness and dizziness.0
822.090.737.091.0NaN79.096.220.0133.01.01.00.0ModerateGoodPatient reports feeling dizzy and shortness of breath.0
967.0106.637.1NaN128.070.097.714.0182.03.00.00.0MildNaNFeeling fine and well today.0
ageweighttemperatureheart_ratesystolic_bpdiastolic_bpoxygen_saturationrespiration_rateblood_glucosepain_levelvomitingdiarrheafatigue_levelsleep_qualityclinical_notesdeteriorated
1499038.0NaN36.5101.0106.063.089.415.0190.03.01.01.0SeverePoorSlight headache in the morning.0
14991NaN83.935.770.0NaN76.0NaN14.0185.04.00.0NaNSevereGoodSlight headache in the morning.0
14992NaN88.536.568.0114.071.090.522.0145.01.01.00.0NoGoodChest tightness and dizziness.0
1499376.0104.935.797.0NaN71.093.823.0182.03.01.00.0NoGoodNo remarkable symptoms today.1
1499423.0100.437.475.0120.061.0NaNNaN194.04.00.01.0SevereNaNNo remarkable symptoms today.0
1499523.098.035.7116.098.069.099.116.0127.05.00.00.0MildGoodComplains of nausea and dizziness.0
1499658.087.836.066.0128.075.097.621.0176.05.01.00.0NoPoorPatient reports feeling dizzy and shortness of breath.0
1499746.093.537.687.0106.069.093.722.082.04.0NaNNaNNoFairSlight headache in the morning.0
1499836.057.536.392.0127.064.092.921.0111.0NaN0.01.0MildNaNRestless sleep but otherwise well.1
1499930.0115.935.867.0NaNNaN98.214.0146.02.01.00.0NaNGoodSlight headache in the morning.0